Business Intelligence generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer, and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information; content delivery infrastructure systems for delivery and management of reports and analytics; and data warehousing systems for cleansing and consolidating information from disparate sources.
In many organizations it is desirable not to require the user of Business Intelligence to understand the complexities of the underlying data source. Within an organization, a range of underlying data sources, such as relational databases, On Line Analytic Processing (OLAP) systems, eXtensible Markup Language (XML) files, Really Simple Syndication (RSS) feeds, and other data sources are used to collect, store, and manage raw data. Therefore, it is advantageous to be able to work with data using a semantic abstraction that provides terms and abstracted logic for dimensions and measures on top of the underlying data. Semantic abstractions for relational databases are known in the art. Semantic abstraction techniques are disclosed in U.S. Pat. No. 5,555,403, the contents of which are incorporated herein by reference. It would be advantageous to enhance known semantic abstractions based on relational data sources and OLAP data sources, such that these semantic layers support hierarchical data in the form of file data such as XML and as streaming data, such as a web service or RSS feed.
Semantic abstractions assist users in creating meaningful queries against the underlying data sources and creating accurate reports without understanding the structure of the underlying data. It would useful to provide semantic abstractions based on hierarchical data.